ardupilot/libraries/AP_NavEKF2/AP_NavEKF2_Measurements.cpp

/// -*- tab-width: 4; Mode: C++; c-basic-offset: 4; indent-tabs-mode: nil -*-

#include <AP_HAL/AP_HAL.h>

#if HAL_CPU_CLASS >= HAL_CPU_CLASS_150
#include "AP_NavEKF2.h"
#include "AP_NavEKF2_core.h"
#include <AP_AHRS/AP_AHRS.h>
#include <AP_Vehicle/AP_Vehicle.h>

#include <stdio.h>

extern const AP_HAL::HAL& hal;


/********************************************************
*              OPT FLOW AND RANGE FINDER                *
********************************************************/

// Read the range finder and take new measurements if available
// Read at 20Hz and apply a median filter
void NavEKF2_core::readRangeFinder(void)
{
    uint8_t midIndex;
    uint8_t maxIndex;
    uint8_t minIndex;
    // get theoretical correct range when the vehicle is on the ground
    rngOnGnd = _rng.ground_clearance_cm() * 0.01f;
    if (_rng.status() == RangeFinder::RangeFinder_Good && (imuSampleTime_ms - lastRngMeasTime_ms) > 50) {
        // store samples and sample time into a ring buffer
        rngMeasIndex ++;
        if (rngMeasIndex > 2) {
            rngMeasIndex = 0;
        }
        storedRngMeasTime_ms[rngMeasIndex] = imuSampleTime_ms;
        storedRngMeas[rngMeasIndex] = _rng.distance_cm() * 0.01f;
        // check for three fresh samples and take median
        bool sampleFresh[3];
        for (uint8_t index = 0; index <= 2; index++) {
            sampleFresh[index] = (imuSampleTime_ms - storedRngMeasTime_ms[index]) < 500;
        }
        if (sampleFresh[0] && sampleFresh[1] && sampleFresh[2]) {
            if (storedRngMeas[0] > storedRngMeas[1]) {
                minIndex = 1;
                maxIndex = 0;
            } else {
                maxIndex = 0;
                minIndex = 1;
            }
            if (storedRngMeas[2] > storedRngMeas[maxIndex]) {
                midIndex = maxIndex;
            } else if (storedRngMeas[2] < storedRngMeas[minIndex]) {
                midIndex = minIndex;
            } else {
                midIndex = 2;
            }
            rngMea = max(storedRngMeas[midIndex],rngOnGnd);
            newDataRng = true;
            rngValidMeaTime_ms = imuSampleTime_ms;
        } else if (onGround) {
            // if on ground and no return, we assume on ground range
            rngMea = rngOnGnd;
            newDataRng = true;
            rngValidMeaTime_ms = imuSampleTime_ms;
        } else {
            newDataRng = false;
        }
        lastRngMeasTime_ms =  imuSampleTime_ms;
    }
}

// write the raw optical flow measurements
// this needs to be called externally.
void NavEKF2_core::writeOptFlowMeas(uint8_t &rawFlowQuality, Vector2f &rawFlowRates, Vector2f &rawGyroRates, uint32_t &msecFlowMeas)
{
    // The raw measurements need to be optical flow rates in radians/second averaged across the time since the last update
    // The PX4Flow sensor outputs flow rates with the following axis and sign conventions:
    // A positive X rate is produced by a positive sensor rotation about the X axis
    // A positive Y rate is produced by a positive sensor rotation about the Y axis
    // This filter uses a different definition of optical flow rates to the sensor with a positive optical flow rate produced by a
    // negative rotation about that axis. For example a positive rotation of the flight vehicle about its X (roll) axis would produce a negative X flow rate
    flowMeaTime_ms = imuSampleTime_ms;
    // calculate bias errors on flow sensor gyro rates, but protect against spikes in data
    // reset the accumulated body delta angle and time
    // don't do the calculation if not enough time lapsed for a reliable body rate measurement
    if (delTimeOF > 0.01f) {
        flowGyroBias.x = 0.99f * flowGyroBias.x + 0.01f * constrain_float((rawGyroRates.x - delAngBodyOF.x/delTimeOF),-0.1f,0.1f);
        flowGyroBias.y = 0.99f * flowGyroBias.y + 0.01f * constrain_float((rawGyroRates.y - delAngBodyOF.y/delTimeOF),-0.1f,0.1f);
        delAngBodyOF.zero();
        delTimeOF = 0.0f;
    }
    // check for takeoff if relying on optical flow and zero measurements until takeoff detected
    // if we haven't taken off - constrain position and velocity states
    if (frontend._fusionModeGPS == 3) {
        detectOptFlowTakeoff();
    }
    // calculate rotation matrices at mid sample time for flow observations
    stateStruct.quat.rotation_matrix(Tbn_flow);
    Tnb_flow = Tbn_flow.transposed();
    // don't use data with a low quality indicator or extreme rates (helps catch corrupt sensor data)
    if ((rawFlowQuality > 0) && rawFlowRates.length() < 4.2f && rawGyroRates.length() < 4.2f) {
        // correct flow sensor rates for bias
        omegaAcrossFlowTime.x = rawGyroRates.x - flowGyroBias.x;
        omegaAcrossFlowTime.y = rawGyroRates.y - flowGyroBias.y;
        // write uncorrected flow rate measurements that will be used by the focal length scale factor estimator
        // note correction for different axis and sign conventions used by the px4flow sensor
        ofDataNew.flowRadXY = - rawFlowRates; // raw (non motion compensated) optical flow angular rate about the X axis (rad/sec)
        // write flow rate measurements corrected for body rates
        ofDataNew.flowRadXYcomp.x = ofDataNew.flowRadXY.x + omegaAcrossFlowTime.x;
        ofDataNew.flowRadXYcomp.y = ofDataNew.flowRadXY.y + omegaAcrossFlowTime.y;
        // record time last observation was received so we can detect loss of data elsewhere
        flowValidMeaTime_ms = imuSampleTime_ms;
        // estimate sample time of the measurement
        ofDataNew.time_ms = imuSampleTime_ms - frontend._flowDelay_ms - frontend.flowTimeDeltaAvg_ms/2;
        // Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
        // This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
        ofDataNew.time_ms = roundToNearest(ofDataNew.time_ms, frontend.fusionTimeStep_ms);
        // Prevent time delay exceeding age of oldest IMU data in the buffer
        ofDataNew.time_ms = max(ofDataNew.time_ms,imuDataDelayed.time_ms);
        // Save data to buffer
        StoreOF();
        // Check for data at the fusion time horizon
        newDataFlow = RecallOF();
    }
}

// store OF data in a history array
void NavEKF2_core::StoreOF()
{
    if (ofStoreIndex >= OBS_BUFFER_LENGTH) {
        ofStoreIndex = 0;
    }
    storedOF[ofStoreIndex] = ofDataNew;
    ofStoreIndex += 1;
}

// return newest un-used optical flow data that has fallen behind the fusion time horizon
// if no un-used data is available behind the fusion horizon, return false
bool NavEKF2_core::RecallOF()
{
    of_elements dataTemp;
    of_elements dataTempZero;
    dataTempZero.time_ms = 0;
    uint32_t temp_ms = 0;
    uint8_t bestIndex = 0;
    for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
        dataTemp = storedOF[i];
        // find a measurement older than the fusion time horizon that we haven't checked before
        if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
            // Find the most recent non-stale measurement that meets the time horizon criteria
            if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
                ofDataDelayed = dataTemp;
                temp_ms = dataTemp.time_ms;
                bestIndex = i;
            }
        }
    }
    if (temp_ms != 0) {
        // zero the time stamp for that piece of data so we won't use it again
        storedOF[bestIndex]=dataTempZero;
        return true;
    } else {
        return false;
    }
}



/********************************************************
*                      MAGNETOMETER                     *
********************************************************/

// return magnetometer offsets
// return true if offsets are valid
bool NavEKF2_core::getMagOffsets(Vector3f &magOffsets) const
{
    // compass offsets are valid if we have finalised magnetic field initialisation and magnetic field learning is not prohibited and primary compass is valid
    if (secondMagYawInit && (frontend._magCal != 2) && _ahrs->get_compass()->healthy(0)) {
        magOffsets = _ahrs->get_compass()->get_offsets(0) - stateStruct.body_magfield*1000.0f;
        return true;
    } else {
        magOffsets = _ahrs->get_compass()->get_offsets(0);
        return false;
    }
}

// check for new magnetometer data and update store measurements if available
void NavEKF2_core::readMagData()
{
    // do not accept new compass data faster than 14Hz (nominal rate is 10Hz) to prevent high processor loading
    // because magnetometer fusion is an expensive step and we could overflow the FIFO buffer
    if (use_compass() && _ahrs->get_compass()->last_update_usec() - lastMagUpdate_us > 70000) {
        // store time of last measurement update
        lastMagUpdate_us = _ahrs->get_compass()->last_update_usec();

        // estimate of time magnetometer measurement was taken, allowing for delays
        magDataNew.time_ms = imuSampleTime_ms - frontend.magDelay_ms;

        // Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
        // This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
        magDataNew.time_ms = roundToNearest(magDataNew.time_ms, frontend.fusionTimeStep_ms);

        // read compass data and scale to improve numerical conditioning
        magDataNew.mag = _ahrs->get_compass()->get_field() * 0.001f;

        // check for consistent data between magnetometers
        consistentMagData = _ahrs->get_compass()->consistent();

        // check if compass offsets have been changed and adjust EKF bias states to maintain consistent innovations
        if (_ahrs->get_compass()->healthy(0)) {
            Vector3f nowMagOffsets = _ahrs->get_compass()->get_offsets(0);
            bool changeDetected = (!is_equal(nowMagOffsets.x,lastMagOffsets.x) || !is_equal(nowMagOffsets.y,lastMagOffsets.y) || !is_equal(nowMagOffsets.z,lastMagOffsets.z));
            // Ignore bias changes before final mag field and yaw initialisation, as there may have been a compass calibration
            if (changeDetected && secondMagYawInit) {
                stateStruct.body_magfield.x += (nowMagOffsets.x - lastMagOffsets.x) * 0.001f;
                stateStruct.body_magfield.y += (nowMagOffsets.y - lastMagOffsets.y) * 0.001f;
                stateStruct.body_magfield.z += (nowMagOffsets.z - lastMagOffsets.z) * 0.001f;
            }
            lastMagOffsets = nowMagOffsets;
        }

        // save magnetometer measurement to buffer to be fused later
        StoreMag();
    }
}
// store magnetometer data in a history array
void NavEKF2_core::StoreMag()
{
    if (magStoreIndex >= OBS_BUFFER_LENGTH) {
        magStoreIndex = 0;
    }
    storedMag[magStoreIndex] = magDataNew;
    magStoreIndex += 1;
}

// return newest un-used magnetometer data that has fallen behind the fusion time horizon
// if no un-used data is available behind the fusion horizon, return false
bool NavEKF2_core::RecallMag()
{
    mag_elements dataTemp;
    mag_elements dataTempZero;
    dataTempZero.time_ms = 0;
    uint32_t temp_ms = 0;
    uint8_t bestIndex = 0;
    for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
        dataTemp = storedMag[i];
        // find a measurement older than the fusion time horizon that we haven't checked before
        if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
            // Find the most recent non-stale measurement that meets the time horizon criteria
            if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
                magDataDelayed = dataTemp;
                temp_ms = dataTemp.time_ms;
                bestIndex = i;
            }
        }
    }
    if (temp_ms != 0) {
        // zero the time stamp for that piece of data so we won't use it again
        storedMag[bestIndex]=dataTempZero;
        return true;
    } else {
        return false;
    }
}




/********************************************************
*                Inertial Measurements                  *
********************************************************/

// update IMU delta angle and delta velocity measurements
void NavEKF2_core::readIMUData()
{
    const AP_InertialSensor &ins = _ahrs->get_ins();

    // average IMU sampling rate
    dtIMUavg = 1.0f/ins.get_sample_rate();

    // the imu sample time is used as a common time reference throughout the filter
    imuSampleTime_ms = hal.scheduler->millis();

    if (ins.use_accel(0) && ins.use_accel(1)) {
        // dual accel mode
        // delta time from each IMU
        float dtDelVel0 = dtIMUavg;
        float dtDelVel1 = dtIMUavg;
        // delta velocity vector from each IMU
        Vector3f delVel0, delVel1;

        // Get delta velocity and time data from each IMU
        readDeltaVelocity(0, delVel0, dtDelVel0);
        readDeltaVelocity(1, delVel1, dtDelVel1);

        // apply a peak hold 0.2 second time constant decaying envelope filter to the noise length on IMU 0
        float alpha = 1.0f - 5.0f*dtDelVel0;
        imuNoiseFiltState0 = maxf(ins.get_vibration_levels(0).length(), alpha*imuNoiseFiltState0);

        // apply a peak hold 0.2 second time constant decaying envelope filter to the noise length on IMU 1
        alpha = 1.0f - 5.0f*dtDelVel1;
        imuNoiseFiltState1 = maxf(ins.get_vibration_levels(1).length(), alpha*imuNoiseFiltState1);

        // calculate the filtered difference between acceleration vectors from IMU 0 and 1
        // apply a LPF filter with a 1.0 second time constant
        alpha = constrain_float(0.5f*(dtDelVel0 + dtDelVel1),0.0f,1.0f);
        accelDiffFilt = (ins.get_accel(0) - ins.get_accel(1)) * alpha + accelDiffFilt * (1.0f - alpha);
        float accelDiffLength = accelDiffFilt.length();

        // Check the difference for excessive error and use the IMU with less noise
        // Apply hysteresis to prevent rapid switching
        if (accelDiffLength > 1.8f || (accelDiffLength > 1.2f && lastImuSwitchState != IMUSWITCH_MIXED)) {
            if (lastImuSwitchState == IMUSWITCH_MIXED) {
                // no previous fail so switch to the IMU with least noise
                if (imuNoiseFiltState0 < imuNoiseFiltState1) {
                    lastImuSwitchState = IMUSWITCH_IMU0;
                    // Get data from IMU 0
                    imuDataNew.delVel = delVel0;
                    imuDataNew.delVelDT = dtDelVel0;
                } else {
                    lastImuSwitchState = IMUSWITCH_IMU1;
                    // Get data from IMU 1
                    imuDataNew.delVel = delVel1;
                    imuDataNew.delVelDT = dtDelVel1;
                }
            } else if (lastImuSwitchState == IMUSWITCH_IMU0) {
                // IMU 1 previously failed so require 5 m/s/s less noise on IMU 1 to switch
                if (imuNoiseFiltState0 - imuNoiseFiltState1 > 5.0f) {
                    // IMU 1 is significantly less noisy, so switch
                    lastImuSwitchState = IMUSWITCH_IMU1;
                    // Get data from IMU 1
                    imuDataNew.delVel = delVel1;
                    imuDataNew.delVelDT = dtDelVel1;
                }
            } else {
                // IMU 0 previously failed so require 5 m/s/s less noise on IMU 0 to switch across
                if (imuNoiseFiltState1 - imuNoiseFiltState0 > 5.0f) {
                    // IMU 0 is significantly less noisy, so switch
                    lastImuSwitchState = IMUSWITCH_IMU0;
                    // Get data from IMU 0
                    imuDataNew.delVel = delVel0;
                    imuDataNew.delVelDT = dtDelVel0;
                }
            }
        } else {
            lastImuSwitchState = IMUSWITCH_MIXED;
            // Use a blend of both accelerometers
            imuDataNew.delVel = (delVel0 + delVel1)*0.5f;
            imuDataNew.delVelDT = (dtDelVel0 + dtDelVel1)*0.5f;
        }
    } else {
        // single accel mode - one of the first two accelerometers are unhealthy, not available or de-selected by the user
        // set the switch state based on the IMU we are using to make the data source selection visible
        if (ins.use_accel(0)) {
            readDeltaVelocity(0, imuDataNew.delVel, imuDataNew.delVelDT);
            lastImuSwitchState = IMUSWITCH_IMU0;
        } else if (ins.use_accel(1)) {
            readDeltaVelocity(1, imuDataNew.delVel, imuDataNew.delVelDT);
            lastImuSwitchState = IMUSWITCH_IMU1;
        } else {
            readDeltaVelocity(ins.get_primary_accel(), imuDataNew.delVel, imuDataNew.delVelDT);
            switch (ins.get_primary_accel()) {
                case 0:
                    lastImuSwitchState = IMUSWITCH_IMU0;
                    break;
                case 1:
                    lastImuSwitchState = IMUSWITCH_IMU1;
                    break;
                default:
                    // we must be using an IMU which can't be properly represented so we set to "mixed"
                    lastImuSwitchState = IMUSWITCH_MIXED;
                    break;
            }
        }
    }

    // Get delta angle data from promary gyro
    readDeltaAngle(ins.get_primary_gyro(), imuDataNew.delAng);
    imuDataNew.delAngDT = max(ins.get_delta_time(),1.0e-4f);

    // get current time stamp
    imuDataNew.time_ms = imuSampleTime_ms;

    // save data in the FIFO buffer
    StoreIMU();

    // extract the oldest available data from the FIFO buffer
    imuDataDelayed = storedIMU[fifoIndexDelayed];

}

// store imu in the FIFO
void NavEKF2_core::StoreIMU()
{
    // increment the index and write new data
    fifoIndexNow = fifoIndexNow + 1;
    if (fifoIndexNow >= IMU_BUFFER_LENGTH) {
        fifoIndexNow = 0;
    }
    storedIMU[fifoIndexNow] = imuDataNew;
    // set the index required to access the oldest data
    fifoIndexDelayed = fifoIndexNow + 1;
    if (fifoIndexDelayed >= IMU_BUFFER_LENGTH) {
        fifoIndexDelayed = 0;
    }
}

// reset the stored imu history and store the current value
void NavEKF2_core::StoreIMU_reset()
{
    // write current measurement to entire table
    for (uint8_t i=0; i<IMU_BUFFER_LENGTH; i++) {
        storedIMU[i] = imuDataNew;
    }
    imuDataDelayed = imuDataNew;
    fifoIndexDelayed = fifoIndexNow+1;
    if (fifoIndexDelayed >= IMU_BUFFER_LENGTH) {
        fifoIndexDelayed = 0;
    }
}

// recall IMU data from the FIFO
void NavEKF2_core::RecallIMU()
{
    imuDataDelayed = storedIMU[fifoIndexDelayed];
}
bool NavEKF2_core::readDeltaVelocity(uint8_t ins_index, Vector3f &dVel, float &dVel_dt) {
    const AP_InertialSensor &ins = _ahrs->get_ins();

    if (ins_index < ins.get_accel_count()) {
        ins.get_delta_velocity(ins_index,dVel);
        dVel_dt = max(ins.get_delta_velocity_dt(ins_index),1.0e-4f);
        return true;
    }
    return false;
}




/********************************************************
*             Global Position Measurement               *
********************************************************/

// check for new valid GPS data and update stored measurement if available
void NavEKF2_core::readGpsData()
{
    // check for new GPS data
    // do not accept data at a faster rate than 14Hz to avoid overflowing the FIFO buffer
    if (_ahrs->get_gps().last_message_time_ms() - lastTimeGpsReceived_ms > 70) {
        if (_ahrs->get_gps().status() >= AP_GPS::GPS_OK_FIX_3D) {
            // report GPS fix status
            gpsCheckStatus.bad_fix = false;

            // store fix time from previous read
            secondLastGpsTime_ms = lastTimeGpsReceived_ms;

            // get current fix time
            lastTimeGpsReceived_ms = _ahrs->get_gps().last_message_time_ms();

            // estimate when the GPS fix was valid, allowing for GPS processing and other delays
            // ideally we should be using a timing signal from the GPS receiver to set this time
            gpsDataNew.time_ms = lastTimeGpsReceived_ms - frontend._gpsDelay_ms;

            // Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
            // This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
            gpsDataNew.time_ms = roundToNearest(gpsDataNew.time_ms, frontend.fusionTimeStep_ms);

            // Prevent time delay exceeding age of oldest IMU data in the buffer
            gpsDataNew.time_ms = max(gpsDataNew.time_ms,imuDataDelayed.time_ms);

            // read the NED velocity from the GPS
            gpsDataNew.vel = _ahrs->get_gps().velocity();

            // Use the speed accuracy from the GPS if available, otherwise set it to zero.
            // Apply a decaying envelope filter with a 5 second time constant to the raw speed accuracy data
            float alpha = constrain_float(0.0002f * (lastTimeGpsReceived_ms - secondLastGpsTime_ms),0.0f,1.0f);
            gpsSpdAccuracy *= (1.0f - alpha);
            float gpsSpdAccRaw;
            if (!_ahrs->get_gps().speed_accuracy(gpsSpdAccRaw)) {
                gpsSpdAccuracy = 0.0f;
            } else {
                gpsSpdAccuracy = max(gpsSpdAccuracy,gpsSpdAccRaw);
            }

            // check if we have enough GPS satellites and increase the gps noise scaler if we don't
            if (_ahrs->get_gps().num_sats() >= 6 && (PV_AidingMode == AID_ABSOLUTE)) {
                gpsNoiseScaler = 1.0f;
            } else if (_ahrs->get_gps().num_sats() == 5 && (PV_AidingMode == AID_ABSOLUTE)) {
                gpsNoiseScaler = 1.4f;
            } else { // <= 4 satellites or in constant position mode
                gpsNoiseScaler = 2.0f;
            }

            // Check if GPS can output vertical velocity and set GPS fusion mode accordingly
            if (_ahrs->get_gps().have_vertical_velocity() && frontend._fusionModeGPS == 0) {
                useGpsVertVel = true;
            } else {
                useGpsVertVel = false;
            }

            // Monitor quality of the GPS velocity data before and after alignment using separate checks
            if (PV_AidingMode != AID_ABSOLUTE) {
                // Pre-alignment checks
                gpsGoodToAlign = calcGpsGoodToAlign();
            } else {
                // Post-alignment checks
                calcGpsGoodForFlight();
            }

            // read latitutde and longitude from GPS and convert to local NE position relative to the stored origin
            // If we don't have an origin, then set it to the current GPS coordinates
            const struct Location &gpsloc = _ahrs->get_gps().location();
            if (validOrigin) {
                gpsDataNew.pos = location_diff(EKF_origin, gpsloc);
            } else if (gpsGoodToAlign) {
                // Set the NE origin to the current GPS position
                setOrigin();
                // Now we know the location we have an estimate for the magnetic field declination and adjust the earth field accordingly
                alignMagStateDeclination();
                // Set the height of the NED origin to ‘height of baro height datum relative to GPS height datum'
                EKF_origin.alt = gpsloc.alt - baroDataNew.hgt;
                // We are by definition at the origin at the instant of alignment so set NE position to zero
                gpsDataNew.pos.zero();
                // If GPS useage isn't explicitly prohibited, we switch to absolute position mode
                if (isAiding && frontend._fusionModeGPS != 3) {
                    PV_AidingMode = AID_ABSOLUTE;
                    // Initialise EKF position and velocity states
                    ResetPosition();
                    ResetVelocity();
                }
            }

            // save measurement to buffer to be fused later
            StoreGPS();

            // declare GPS available for use
            gpsNotAvailable = false;
        } else {
            // report GPS fix status
            gpsCheckStatus.bad_fix = true;
        }
    }

    // We need to handle the case where GPS is lost for a period of time that is too long to dead-reckon
    // If that happens we need to put the filter into a constant position mode, reset the velocity states to zero
    // and use the last estimated position as a synthetic GPS position

    // check if we can use opticalflow as a backup
    bool optFlowBackupAvailable = (flowDataValid && !hgtTimeout);

    // Set GPS time-out threshold depending on whether we have an airspeed sensor to constrain drift
    uint16_t gpsRetryTimeout_ms = useAirspeed() ? frontend.gpsRetryTimeUseTAS_ms : frontend.gpsRetryTimeNoTAS_ms;

    // Set the time that copters will fly without a GPS lock before failing the GPS and switching to a non GPS mode
    uint16_t gpsFailTimeout_ms = optFlowBackupAvailable ? frontend.gpsFailTimeWithFlow_ms : gpsRetryTimeout_ms;

    // If we haven't received GPS data for a while and we are using it for aiding, then declare the position and velocity data as being timed out
    if (imuSampleTime_ms - lastTimeGpsReceived_ms > gpsFailTimeout_ms) {

        // Let other processes know that GPS is not available and that a timeout has occurred
        posTimeout = true;
        velTimeout = true;
        gpsNotAvailable = true;

        // If we are totally reliant on GPS for navigation, then we need to switch to a non-GPS mode of operation
        // If we don't have airspeed or sideslip assumption or optical flow to constrain drift, then go into constant position mode.
        // If we can do optical flow nav (valid flow data and height above ground estimate), then go into flow nav mode.
        if (PV_AidingMode == AID_ABSOLUTE && !useAirspeed() && !assume_zero_sideslip()) {
            if (optFlowBackupAvailable) {
                // we can do optical flow only nav
                frontend._fusionModeGPS = 3;
                PV_AidingMode = AID_RELATIVE;
            } else {
                // store the current position
                lastKnownPositionNE.x = stateStruct.position.x;
                lastKnownPositionNE.y = stateStruct.position.y;

                // put the filter into constant position mode
                PV_AidingMode = AID_NONE;

                // Reset the velocity and position states
                ResetVelocity();
                ResetPosition();

                // Reset the normalised innovation to avoid false failing bad fusion tests
                velTestRatio = 0.0f;
                posTestRatio = 0.0f;
            }
        }
    }
}


// store GPS data in a history array
void NavEKF2_core::StoreGPS()
{
    if (gpsStoreIndex >= OBS_BUFFER_LENGTH) {
        gpsStoreIndex = 0;
    }
    storedGPS[gpsStoreIndex] = gpsDataNew;
    gpsStoreIndex += 1;
}

// return newest un-used GPS data that has fallen behind the fusion time horizon
// if no un-used data is available behind the fusion horizon, return false
bool NavEKF2_core::RecallGPS()
{
    gps_elements dataTemp;
    gps_elements dataTempZero;
    dataTempZero.time_ms = 0;
    uint32_t temp_ms = 0;
    uint8_t bestIndex;
    for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
        dataTemp = storedGPS[i];
        // find a measurement older than the fusion time horizon that we haven't checked before
        if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
            // Find the most recent non-stale measurement that meets the time horizon criteria
            if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
                gpsDataDelayed = dataTemp;
                temp_ms = dataTemp.time_ms;
                bestIndex = i;
            }
        }
    }
    if (temp_ms != 0) {
        // zero the time stamp for that piece of data so we won't use it again
        storedGPS[bestIndex]=dataTempZero;
        return true;
    } else {
        return false;
    }
}


bool NavEKF2_core::readDeltaAngle(uint8_t ins_index, Vector3f &dAng) {
    const AP_InertialSensor &ins = _ahrs->get_ins();

    if (ins_index < ins.get_gyro_count()) {
        ins.get_delta_angle(ins_index,dAng);
        return true;
    }
    return false;
}


/********************************************************
*                  Height Measurements                  *
********************************************************/

// check for new altitude measurement data and update stored measurement if available
void NavEKF2_core::readHgtData()
{
    // check to see if baro measurement has changed so we know if a new measurement has arrived
    // do not accept data at a faster rate than 14Hz to avoid overflowing the FIFO buffer
    if (_baro.get_last_update() - lastHgtReceived_ms > 70) {
        // Don't use Baro height if operating in optical flow mode as we use range finder instead
        if (frontend._fusionModeGPS == 3 && frontend._altSource == 1) {
            if ((imuSampleTime_ms - rngValidMeaTime_ms) < 2000) {
                // adjust range finder measurement to allow for effect of vehicle tilt and height of sensor
                baroDataNew.hgt = max(rngMea * Tnb_flow.c.z, rngOnGnd);
                // calculate offset to baro data that enables baro to be used as a backup
                // filter offset to reduce effect of baro noise and other transient errors on estimate
                baroHgtOffset = 0.1f * (_baro.get_altitude() + stateStruct.position.z) + 0.9f * baroHgtOffset;
            } else if (isAiding && takeOffDetected) {
                // we have lost range finder measurements and are in optical flow flight
                // use baro measurement and correct for baro offset - failsafe use only as baro will drift
                baroDataNew.hgt = max(_baro.get_altitude() - baroHgtOffset, rngOnGnd);
            } else {
                // If we are on ground and have no range finder reading, assume the nominal on-ground height
                baroDataNew.hgt = rngOnGnd;
                // calculate offset to baro data that enables baro to be used as a backup
                // filter offset to reduce effect of baro noise and other transient errors on estimate
                baroHgtOffset = 0.1f * (_baro.get_altitude() + stateStruct.position.z) + 0.9f * baroHgtOffset;
            }
        } else {
            // Normal operation is to use baro measurement
            baroDataNew.hgt = _baro.get_altitude();
        }

        // filtered baro data used to provide a reference for takeoff
        // it is is reset to last height measurement on disarming in performArmingChecks()
        if (!getTakeoffExpected()) {
            const float gndHgtFiltTC = 0.5f;
            const float dtBaro = frontend.hgtAvg_ms*1.0e-3f;
            float alpha = constrain_float(dtBaro / (dtBaro+gndHgtFiltTC),0.0f,1.0f);
            meaHgtAtTakeOff += (baroDataDelayed.hgt-meaHgtAtTakeOff)*alpha;
        } else if (isAiding && getTakeoffExpected()) {
            // If we are in takeoff mode, the height measurement is limited to be no less than the measurement at start of takeoff
            // This prevents negative baro disturbances due to copter downwash corrupting the EKF altitude during initial ascent
            baroDataNew.hgt = max(baroDataNew.hgt, meaHgtAtTakeOff);
        }

        // time stamp used to check for new measurement
        lastHgtReceived_ms = _baro.get_last_update();

        // estimate of time height measurement was taken, allowing for delays
        baroDataNew.time_ms = lastHgtReceived_ms - frontend._hgtDelay_ms;

        // Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
        // This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
        baroDataNew.time_ms = roundToNearest(baroDataNew.time_ms, frontend.fusionTimeStep_ms);

        // Prevent time delay exceeding age of oldest IMU data in the buffer
        baroDataNew.time_ms = max(baroDataNew.time_ms,imuDataDelayed.time_ms);

        // save baro measurement to buffer to be fused later
        StoreBaro();
    }
}

// store baro in a history array
void NavEKF2_core::StoreBaro()
{
    if (baroStoreIndex >= OBS_BUFFER_LENGTH) {
        baroStoreIndex = 0;
    }
    storedBaro[baroStoreIndex] = baroDataNew;
    baroStoreIndex += 1;
}

// return newest un-used baro data that has fallen behind the fusion time horizon
// if no un-used data is available behind the fusion horizon, return false
bool NavEKF2_core::RecallBaro()
{
    baro_elements dataTemp;
    baro_elements dataTempZero;
    dataTempZero.time_ms = 0;
    uint32_t temp_ms = 0;
    uint8_t bestIndex = 0;
    for (uint8_t i=0; i<OBS_BUFFER_LENGTH; i++) {
        dataTemp = storedBaro[i];
        // find a measurement older than the fusion time horizon that we haven't checked before
        if (dataTemp.time_ms != 0 && dataTemp.time_ms <= imuDataDelayed.time_ms) {
            // Find the most recent non-stale measurement that meets the time horizon criteria
            if (((imuDataDelayed.time_ms - dataTemp.time_ms) < 500) && dataTemp.time_ms > temp_ms) {
                baroDataDelayed = dataTemp;
                temp_ms = dataTemp.time_ms;
                bestIndex = i;
            }
        }
    }
    if (temp_ms != 0) {
        // zero the time stamp for that piece of data so we won't use it again
        storedBaro[bestIndex]=dataTempZero;
        return true;
    } else {
        return false;
    }
}



/********************************************************
*                Air Speed Measurements                 *
********************************************************/

// check for new airspeed data and update stored measurements if available
void NavEKF2_core::readAirSpdData()
{
    // if airspeed reading is valid and is set by the user to be used and has been updated then
    // we take a new reading, convert from EAS to TAS and set the flag letting other functions
    // know a new measurement is available
    const AP_Airspeed *aspeed = _ahrs->get_airspeed();
    if (aspeed &&
            aspeed->use() &&
            aspeed->last_update_ms() != timeTasReceived_ms) {
        tasDataNew.tas = aspeed->get_airspeed() * aspeed->get_EAS2TAS();
        timeTasReceived_ms = aspeed->last_update_ms();
        tasDataNew.time_ms = timeTasReceived_ms - frontend.tasDelay_ms;
        // Assign measurement to nearest fusion interval so that multiple measurements can be fused on the same frame
        // This allows us to perform the covariance prediction over longer time steps which reduces numerical precision errors
        tasDataNew.time_ms = roundToNearest(tasDataNew.time_ms, frontend.fusionTimeStep_ms);
        newDataTas = true;
        StoreTAS();
        RecallTAS();
    } else {
        newDataTas = false;
    }
}

// Round to the nearest multiple of a integer
uint32_t NavEKF2_core::roundToNearest(uint32_t dividend, uint32_t divisor )
{
  return ((uint32_t)round((float)dividend/float(divisor)))*divisor;
}

#endif // HAL_CPU_CLASS